class_docstr: Planet Earth Landscape problem defined in
  Machine Learning and Data Analysis (MLDA) Problem Set v1.0, Gallagher et al., 2018, PPSN
  https://drive.google.com/file/d/1fc1sVwoLJ0LsQ5fzi4jo3rDJHQ6VGQ1h/view
  and reverted to look for the minimum value (0). This is problem P4.
  
  Parameters
  ----------
  transform: None, "gaussian" or "square"
      whether use the image [0, 4319]x[0, 2159] (None) or to use a Gaussian transform.
  
  Note
  ----
  - the initial image is 4320x2160
  - sampling outside yields a +inf value (except for Gaussian, since large values are mapped to border indices)
  - the image is actually a variant of the one proposed in the article. Indeed, this image
    has a much better z-resolution. It is not directly proportional to the altitude though
    since it is an artificial rescaling to greyscale of a color image.
code: def __init__(self, transform: tp.Optional[str] = None) -> None:
      super().__init__(
          self._get_pixel_value,
          p.Instrumentation(p.Scalar(), p.Scalar()).set_name(
              "standard"
          ),
      )
      self._image = datasets.get_data("Landscape")
      if transform == "gaussian":
          variables = list(
              p.TransitionChoice(list(range(x)))
              for x in self._image.shape
          )
          self.parametrization = p.Instrumentation(*variables).set_name(
              "gaussian"
          )
      elif transform == "square":
          stds = (np.array(self._image.shape) - 1.0) / 2.0
          variables2 = list(
              p.Scalar(init=s).set_mutation(sigma=s) for s in stds
          )
          self.parametrization = p.Instrumentation(
              *variables2
          ).set_name(
              "square"
          )  # maybe buggy, try again?
      elif transform is not None:
          raise ValueError(f"Unknown transform {transform}")
      self._descriptors.pop(
          "transform", None
      )  # automatically added by __new__, but not needed
      self._max = float(self._image.max())
  
imports: import numpy
  import pandas
  import scipy.spatial
  from nevergrad.parametrization import parameter
  import nevergrad.common.typing
  from ..base import ExperimentFunction
  from .None import datasets
label_desc: Use image node_id 1 to create a new Landscape object from inherited base classes: ExperimentFunction with example: obj = Landscape(transform)